


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						(IPNDMScheduler,)
    __magic_name__         =						(('num_inference_steps', 50),)
    def 						a_     (			self     ,   **__snake_case       ):
         snake_case											=							{'''num_train_timesteps''': 1_0_0_0}
         config.update(**__snake_case       )
         return config
    def 						a_     (			self     ,   __snake_case=0     ,   **__snake_case       ):
         snake_case											=							dict(self.forward_default_kwargs       )
         snake_case											=							kwargs.pop('''num_inference_steps'''     ,   __snake_case       )
         snake_case											=							self.dummy_sample
         snake_case											=							0.1 * sample
         snake_case											=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              snake_case											=							self.get_scheduler_config(**__snake_case       )
              snake_case											=							scheduler_class(**__snake_case       )
              scheduler.set_timesteps(__snake_case       )
              # copy over dummy past residuals
              snake_case											=							dummy_past_residuals[:]

              if time_step is None:
                   snake_case											=							scheduler.timesteps[len(scheduler.timesteps       ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__snake_case       )
                   snake_case											=							scheduler_class.from_pretrained(__snake_case       )
                   new_scheduler.set_timesteps(__snake_case       )
                   # copy over dummy past residuals
                   snake_case											=							dummy_past_residuals[:]

              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample
              snake_case											=							new_scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"

              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample
              snake_case											=							new_scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
    def 						a_     (			self       ):
         pass
    def 						a_     (			self     ,   __snake_case=0     ,   **__snake_case       ):
         snake_case											=							dict(self.forward_default_kwargs       )
         snake_case											=							kwargs.pop('''num_inference_steps'''     ,   __snake_case       )
         snake_case											=							self.dummy_sample
         snake_case											=							0.1 * sample
         snake_case											=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              snake_case											=							self.get_scheduler_config()
              snake_case											=							scheduler_class(**__snake_case       )
              scheduler.set_timesteps(__snake_case       )

              # copy over dummy past residuals (must be after setting timesteps)
              snake_case											=							dummy_past_residuals[:]

              if time_step is None:
                   snake_case											=							scheduler.timesteps[len(scheduler.timesteps       ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__snake_case       )
                   snake_case											=							scheduler_class.from_pretrained(__snake_case       )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(__snake_case       )

                   # copy over dummy past residual (must be after setting timesteps)
                   snake_case											=							dummy_past_residuals[:]

              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample
              snake_case											=							new_scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"

              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample
              snake_case											=							new_scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
    def 						a_     (			self     ,   **__snake_case       ):
         snake_case											=							self.scheduler_classes[0]
         snake_case											=							self.get_scheduler_config(**__snake_case       )
         snake_case											=							scheduler_class(**__snake_case       )

         snake_case											=							1_0
         snake_case											=							self.dummy_model()
         snake_case											=							self.dummy_sample_deter
         scheduler.set_timesteps(__snake_case       )

         for i, t in enumerate(scheduler.timesteps       ):
              snake_case											=							model(__snake_case     ,   __snake_case       )
              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case       ).prev_sample

         for i, t in enumerate(scheduler.timesteps       ):
              snake_case											=							model(__snake_case     ,   __snake_case       )
              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case       ).prev_sample

         return sample
    def 						a_     (			self       ):
         snake_case											=							dict(self.forward_default_kwargs       )

         snake_case											=							kwargs.pop('''num_inference_steps'''     ,   __snake_case       )

         for scheduler_class in self.scheduler_classes:
              snake_case											=							self.get_scheduler_config()
              snake_case											=							scheduler_class(**__snake_case       )

              snake_case											=							self.dummy_sample
              snake_case											=							0.1 * sample

              if num_inference_steps is not None and hasattr(__snake_case     ,   '''set_timesteps'''       ):
                   scheduler.set_timesteps(__snake_case       )
              elif num_inference_steps is not None and not hasattr(__snake_case     ,   '''set_timesteps'''       ):
                   snake_case											=							num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              snake_case											=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              snake_case											=							dummy_past_residuals[:]

              snake_case											=							scheduler.timesteps[5]
              snake_case											=							scheduler.timesteps[6]

              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample
              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              self.assertEqual(output_a.shape     ,   sample.shape       )
              self.assertEqual(output_a.shape     ,   output_a.shape       )

              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample
              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              self.assertEqual(output_a.shape     ,   sample.shape       )
              self.assertEqual(output_a.shape     ,   output_a.shape       )
    def 						a_     (			self       ):
         for timesteps in [1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=__snake_case     ,   time_step=__snake_case       )
    def 						a_     (			self       ):
         for t, num_inference_steps in zip([1, 5, 1_0]     ,   [1_0, 5_0, 1_0_0]       ):
              self.check_over_forward(num_inference_steps=__snake_case     ,   time_step=__snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.full_loop()
         snake_case											=							torch.mean(torch.abs(__snake_case       )       )

         assert abs(result_mean.item() - 2_5_4_0_5_2_9       ) < 1_0








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




